Data Science Opportunities

at  Intuit

OVERVIEW

Intuit is a global technology platform that helps consumers and small businesses overcome their most important financial challenges. Serving more than 100 million customers worldwide with TurboTax, Credit Karma, QuickBooks, and Mailchimp, we believe that everyone should have the opportunity to prosper. We never stop working to find new, innovative ways to make that possible.

RESPONSIBILITIES

• Practices leadership and communication skills to influence teams and to evangelize data science across the organization
• Collaborates with stakeholders to define success criteria and align model metrics with business goals. Works side-by-side with product managers, software engineers, and designers in designing experiments and minimum viable products
• Leads technical work of a scrum team: initiating and designing model solutions, driving end-to-end architecture designs of the team’s work, and holding the team accountable for high quality code, git, design, costs and implementation standards
• Performs hands-on data analysis and modeling with large data sets, including discovering data sources, getting data access, cleaning up data, and making them “model-ready”. You need to be willing and able to do your own ETL and design/build featurization
• Applies data mining, NLP, and machine learning (such as supervised/unsupervised, Causal-ML, Online Learning, Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and datasets
• Runs A/B tests to draw conclusions on the impact of your team’s work and communicates results to peers and leaders
• Communicates with partners to ensure successful delivery and integration of DS solutions
• Proactively researches, explores, and enables new ML technologies. Keeps up with the new developments in academia and industry and considers possible extensions to solve Intuit customer problems

All roles require:

• Hands-on expertise in ML paradigms such as Causal-ML, supervised/unsupervised, Online, Bayesian, Reinforcement or Deep Learning.
• Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization
• Proficient in NLP techniques, Explainable AI, and ML frameworks
• Expertise in modern advanced analytical tools and programming languages such as Python, Scala, Java and/or R
• Efficient in SQL, Hive, SparkSQL, etc
• Comfortable working in a Linux environment
• Experience with building end-to-end reusable pipelines from data acquisition to model output delivery
• Quick learner, adaptable, with the ability to work independently in a fast-paced environment
• Strong oral and written communication skills. Ability to conduct meetings and make professional presentations, and to explain complex concepts and technical material to non-technical user